# A Multi-constraint and Multi-objective Allocation Model for Emergency Rescue in IoT Environment

arXiv: https://arxiv.org/abs/2403.10299 · HTML: https://mlsanity.com/paper/2403.10299/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 29 references that are in the map.
- First posted on arXiv 15 Mar 2024.

## Built on by (most cited first, 2 of 2)

- [High-Quality Pseudo-Label Generation Based on Visual Prompt Assisted Cloud Model Update](https://mlsanity.com/paper/2504.00526/) (2025)
- [Catastrophic Forgetting Mitigation via Discrepancy-Weighted Experience Replay](https://mlsanity.com/paper/2507.00042/) (2025)

## Abstract (arXiv)

Emergency relief operations are essential in disaster aftermaths, necessitating effective resource allocation to minimize negative impacts and maximize benefits. In prolonged crises or extensive disasters, a systematic, multi-cycle approach is key for timely and informed decision-making. Leveraging advancements in IoT and spatio-temporal data analytics, we've developed the Multi-Objective Shuffled Gray-Wolf Frog Leaping Model (MSGW-FLM). This multi-constraint, multi-objective resource allocation model has been rigorously tested against 28 diverse challenges, showing superior performance in comparison to established models such as NSGA-II, IBEA, and MOEA/D. MSGW-FLM's effectiveness is particularly notable in complex, multi-cycle emergency rescue scenarios, which involve numerous constraints and objectives. This model represents a significant step forward in optimizing resource distribution in emergency response situations.